


import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

       from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
       _SCREAMING_SNAKE_CASE     :			Optional[int]        =							False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor



class        A     (		unittest.TestCase	):







     '''simple docstring'''







     def __init__(				self    :							Any			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							int=7			,				_UpperCamelCase    :							Optional[Any]=3			,				_UpperCamelCase    :							Dict=18			,				_UpperCamelCase    :							Optional[int]=30			,				_UpperCamelCase    :							List[Any]=400			,				_UpperCamelCase    :							Tuple=None			,				_UpperCamelCase    :							Any=True			,				_UpperCamelCase    :							Tuple=True			,				_UpperCamelCase    :							Optional[int]=None			,				):
      _lowercase:       str						=				size if size is not None else {'height': 20, 'width': 20}
      _lowercase:       List[Any]						=				parent
      _lowercase:       int						=				batch_size
      _lowercase:       int						=				num_channels
      _lowercase:       Any						=				image_size
      _lowercase:       Any						=				min_resolution
      _lowercase:       Optional[Any]						=				max_resolution
      _lowercase:       Dict						=				size
      _lowercase:       Dict						=				do_normalize
      _lowercase:       str						=				do_convert_rgb
      _lowercase:       Any						=				[512, 1_024, 2_048, 4_096]
      _lowercase:       Dict						=				patch_size if patch_size is not None else {'height': 16, 'width': 16}

     def 					UpperCAmelCase__       (				self    :							Tuple):
      return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       List[str]						=				'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
      _lowercase:       Tuple						=				Image.open(requests.get(__snake_case			,				stream=__snake_case).raw).convert("RGB")
      return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	, reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""	, )
@require_torch
@require_vision
class        A     (		lowerCamelCase__	, unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						Optional[Any]            =   PixaStructImageProcessor if is_vision_available() else None

     def 					UpperCAmelCase__       (				self    :							Tuple):
      _lowercase:       int						=				PixaStructImageProcessingTester(self)

     @property
     def 					UpperCAmelCase__       (				self    :							str):
      return self.image_processor_tester.prepare_image_processor_dict()

     def 					UpperCAmelCase__       (				self    :							Any):
      _lowercase:       str						=				self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__snake_case			,				"do_normalize"))
      self.assertTrue(hasattr(__snake_case			,				"do_convert_rgb"))

     def 					UpperCAmelCase__       (				self    :							str):
      _lowercase:       Optional[int]						=				self.image_processor_tester.prepare_dummy_image()

      _lowercase:       Any						=				self.image_processing_class(**self.image_processor_dict)
      _lowercase:       List[Any]						=				2_048

      _lowercase:       List[Any]						=				image_processor(__snake_case			,				return_tensors="pt"			,				max_patches=__snake_case)
      self.assertTrue(torch.allclose(inputs.flattened_patches.mean()			,				torch.tensor(0.0_6_0_6)			,				atol=1e-3			,				rtol=1e-3))

     def 					UpperCAmelCase__       (				self    :							Dict):
      # Initialize image_processor
      _lowercase:       int						=				self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowercase:       Any						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				Image.Image)

      # Test not batched input
      _lowercase:       Union[str, Any]						=				(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       _lowercase:       Any						=				image_processor(
           image_inputs[0]			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(1, max_patch, expected_hidden_dim)			,				)

       # Test batched
       _lowercase:       str						=				image_processor(
           __snake_case			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,				)

     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      # Initialize image_processor
      _lowercase:       List[Any]						=				self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowercase:       Tuple						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				Image.Image)

      # Test not batched input
      _lowercase:       Dict						=				(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      _lowercase:       Union[str, Any]						=				True

      for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       with self.assertRaises(__snake_case):
        _lowercase:       int						=				image_processor(
            image_inputs[0]			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches

       _lowercase:       Union[str, Any]						=				'Hello'

       _lowercase:       int						=				image_processor(
           image_inputs[0]			,				return_tensors="pt"			,				max_patches=__snake_case			,				header_text=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(1, max_patch, expected_hidden_dim)			,				)

       # Test batched
       _lowercase:       Any						=				image_processor(
           __snake_case			,				return_tensors="pt"			,				max_patches=__snake_case			,				header_text=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,				)

     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      # Initialize image_processor
      _lowercase:       Tuple						=				self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      _lowercase:       str						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case			,				numpify=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				np.ndarray)

      _lowercase:       int						=				(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       _lowercase:       List[str]						=				image_processor(
           image_inputs[0]			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(1, max_patch, expected_hidden_dim)			,				)

       # Test batched
       _lowercase:       Any						=				image_processor(
           __snake_case			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,				)






     def 					UpperCAmelCase__       (				self    :							int):
      # Initialize image_processor
      _lowercase:       List[str]						=				self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      _lowercase:       List[Any]						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case			,				torchify=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				torch.Tensor)

      # Test not batched input
      _lowercase:       str						=				(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * self.image_processor_tester.num_channels
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       _lowercase:       List[str]						=				image_processor(
           image_inputs[0]			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(1, max_patch, expected_hidden_dim)			,				)

       # Test batched
       _lowercase:       Union[str, Any]						=				image_processor(
           __snake_case			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,				)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11	, reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""	, )
@require_torch
@require_vision
class        A     (		lowerCamelCase__	, unittest.TestCase	):







     '''simple docstring'''







     lowerCamelCase :						List[Any]            =   PixaStructImageProcessor if is_vision_available() else None

     def 					UpperCAmelCase__       (				self    :							Dict):
      _lowercase:       Any						=				PixaStructImageProcessingTester(self			,				num_channels=4)
      _lowercase:       Union[str, Any]						=				3

     @property
     def 					UpperCAmelCase__       (				self    :							int):
      return self.image_processor_tester.prepare_image_processor_dict()

     def 					UpperCAmelCase__       (				self    :							Tuple):
      _lowercase:       Union[str, Any]						=				self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(__snake_case			,				"do_normalize"))
      self.assertTrue(hasattr(__snake_case			,				"do_convert_rgb"))






     def 					UpperCAmelCase__       (				self    :							Optional[Any]):
      # Initialize image_processor
      _lowercase:       Dict						=				self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      _lowercase:       Optional[Any]						=				prepare_image_inputs(self.image_processor_tester			,				equal_resolution=__snake_case)
      for image in image_inputs:
       self.assertIsInstance(__snake_case			,				Image.Image)

      # Test not batched input
      _lowercase:       Optional[Any]						=				(
          (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
          * (self.image_processor_tester.num_channels - 1)
      ) + 2

      for max_patch in self.image_processor_tester.max_patches:
       # Test not batched input
       _lowercase:       Tuple						=				image_processor(
           image_inputs[0]			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(1, max_patch, expected_hidden_dim)			,				)

       # Test batched
       _lowercase:       Optional[Any]						=				image_processor(
           __snake_case			,				return_tensors="pt"			,				max_patches=__snake_case).flattened_patches
       self.assertEqual(
           encoded_images.shape			,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)			,				)






'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model